Detecting multicollinearity (thanks to ChatGPT)

I just asked ChatGPT how to detect and handle multicollinearity (stg I probably already knew before). Here's the solution code:

import pandas as pd

import numpy as np

from statsmodels.stats.outliers_influence import variance_inflation_factor

# Load data into a pandas DataFrame

data = pd.read_csv('your_data.csv')

# Create a list of column names to check for multicollinearity

cols_to_check = ['feature1', 'feature2', 'feature3', ...]

# Create a new DataFrame with only the columns to check

data_to_check = data[cols_to_check]

# Calculate the VIF scores for each feature

vif_scores = pd.DataFrame()

vif_scores["Feature"] = data_to_check.columns

vif_scores["VIF"] = [variance_inflation_factor(data_to_check.values, i) for i in range(data_to_check.shape[1])]

# Print the VIF scores

print(vif_scores)